Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains. Boolean Tensor Decomposition (BTD) factorizes a binary tensor into the Boolean sum of multiple rank-1 tensors, which is an NP-hard problem. Existing BTD methods have been limited by their high computational cost, in applications to large scale or higher order tensors. In this work, we presented a computationally efficient BTD algorithm, namely \textit{Geometric Expansion for all-order Tensor Factorization} (GETF), that sequentially identifies the rank-1 basis components for a tensor from a geometric perspective. We conducted rigorous theoretical analysis on the validity as well as algorithemic efficiency of GETF in decomposing all-order tensor. Experiments on both synthetic and real-world data demonstrated that GETF has significantly improved performance in reconstruction accuracy, extraction of latent structures and it is an order of magnitude faster than other state-of-the-art methods.

El-Sisi called for upgrading post offices nationwide, as well as automating the litigation system as per the "Digital Egypt Justice" strategy, presidential spokesman Bassam Rady said in a statement following the meeting. El-Sisi demanded the "immediate" start of the "Digital Egypt" initiative, which he said would directly contribute to the country's efforts in digital transformation, and enhance the digital skills of human resources to provide Egyptians with quality digitised services. Minister Talaat reviewed the latest developments concerning launching a platform for urban community models that are meant to govern the construction system in Egypt and deter building violations and land transgressions. The meeting also touched upon the latest domestic projects in electronic design and manufacturing, which is part of the state's vision to assist companies that work in the field to reach international markets, attract direct foreign investments and enhance electronic trade, according to the statement. The meeting also discussed the situation of other national projects in the communications and information technology sectors.

Network pruning is widely used to lighten and accelerate neural network models. Structured network pruning discards the whole neuron or filter, leading to accuracy loss. In this work, we propose a novel concept of neuron merging applicable to both fully connected layers and convolution layers, which compensates for the information loss due to the pruned neurons/filters. Neuron merging starts with decomposing the original weights into two matrices/tensors. One of them becomes the new weights for the current layer, and the other is what we name a scaling matrix, guiding the combination of neurons. If the activation function is ReLU, the scaling matrix can be absorbed into the next layer under certain conditions, compensating for the removed neurons. We also propose a data-free and inexpensive method to decompose the weights by utilizing the cosine similarity between neurons. Compared to the pruned model with the same topology, our merged model better preserves the output feature map of the original model; thus, it maintains the accuracy after pruning without fine-tuning. We demonstrate the effectiveness of our approach over network pruning for various model architectures and datasets. As an example, for VGG-16 on CIFAR-10, we achieve an accuracy of 93.16% while reducing 64% of total parameters, without any fine-tuning. The code can be found here: https://github.com/friendshipkim/neuron-merging

Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.

We fulfil all your research needs spanning across industry verticals with our huge collection of market research reports. We provide our services to all sizes of organisations and across all industry verticals and markets. Our Research Coordinators have in-depth knowledge of reports as well as publishers and will assist you in making an informed decision by giving you unbiased and deep insights on which reports will satisfy your needs at the best price.

A new research Titled "Global Artificial Intelligence Platform Market 2020 Research Report" provides the Professional and In-depth evaluation of scope of current and future market and review of Product Specification, market trend, product type and production analysis considering major factors such as Facts and figure, revenue generated from the sales of this Report, market share and growth rate for each type and application, Gross Margin, key factors driving to the market. The Artificial Intelligence Platform market will reach Volume Million USD in 2019 and CAGR xx% 2015-2019. The report Primarly enlists the basic details of industry based on the fundamental overview of Artificial Intelligence Platform market chain structure, and describes industry surroundings, the development of the market through upstream & downstream, industry overall, investment analysis, manufacturing cost structure, industry policies, plans and development, key players will drive key business decisions and makes a scientific prediction for the development industry prospects on the basis of past, present and forecast data related to the Artificial Intelligence Platform market from 2020-2024. The Scope of the global Artificial Intelligence Platform market mainly focuses on globally, it primarily covers the Artificial Intelligence Platform Market in USA, Canada and Mexico, Artificial Intelligence Platform Market in Germany, France, UK, Russia and Italy, global Artificial Intelligence Platform market in China, Japan, Korea, India and Southeast Asia, global Artificial Intelligence Platform market in Brazil, Argentina, Columbia, Global market in Saudi Arabia, UAE, Egypt, Nigeria and South Africa. The firstly global Artificial Intelligence Platform market describes the market overview, Upstream, Technology, Cost Structure.

Computing technology has become pervasive and with it the expectation for its ready availability when needed, thus basically at all times. Dependability is the set of techniques to build, configure, operate, and manage computer systems to ensure that they are reliable, available, safe, and secure.1 But alas, faults seem to be inherent to computer systems. Components can simply crash or produce incorrect output due to hardware or software bugs or can be invaded by impostors that orchestrate their behavior. Fault tolerance is the ability to enable a system as a whole to continue operating correctly and with acceptable performance, even if some of its components are faulty.3 Fault tolerance is not new; von Neumann himself designed techniques for computers to survive faults.4

Latin America, with its rich and varied cultural heritage, is a region widely known by its diverse musical rhythms. Indeed, music and dance constitute an important part of Latin American cultural assets and identity.2 Some of these rhythms, although famous worldwide, belong to specific regions; for example, samba is from Brazil, tango is from Argentina, merengue is from the Dominican Republic, corrido is from Mexico and vallenato is from Colombia, among many other examples. Most of them were created by the cultural interaction between people from African, Native American, and European cultures that shared their music and instruments. Those heterogeneous cultural characteristics made these music styles appealing to an international audience.

How are Machine Learning Models going to change the Payments Industry? It wasn't so long ago that CEO's and large commercial banks were convinced that more bank locations would always be necessary to service and acquire new customers. However, in the last ten or five years we have seen an emergence of Digital Banks, that have never and will probably never own a physical location, but still manage to grow their user base and add additional services including insurance, mortgages, and loans. In the Banking industry, we have seen companies like First Bank of Nigeria, United Bank of Africa, Zenith Bank, Guaranty Trust Bank dominate for well over twenty years. However, just like the digitization of banking has forced incumbents to change their strategies, the digitization of payments has provided companies like Flutterwave, Paystack, Remita and lately even Korapay to take up some of the market shares, not by focusing on traditional businesses, but by focusing on startups who have grown to overshadow and sometimes even bankrupt traditional businesses.

To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. In particular, we investigate how well NPI effectiveness estimates generalise to unseen countries, and their sensitivity to unobserved factors. Models that account for noise in disease transmission compare favourably. We further evaluate how robust estimates are to different choices of epidemiological parameters and data. Focusing on models that assume transmission noise, we find that previously published results are robust across these choices and across different models. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold.